Sentence recognition apparatus, sentence recognition method, program, and medium

ABSTRACT

In the prior art, it has been difficult to perform proper sentence recognition by using speech recognition or text sentence recognition. The present invention provides a sentence recognition apparatus comprising: a data base for storing a plurality of predetermined standard content word pairs each formed from a plurality of predetermined content words; a speech recognition means of recognizing an input sentence made up of a plurality of words; a content word selection means of selecting content words from among the plurality of words forming the recognized sentence; a judging means of judging whether a content word pair arbitrarily formed from the selected content words matches any one of the standard content word pairs stored in the data base; and an erroneously recognized content word determining means  105  of determining, based on the result of the judgement, an erroneously recognized content word for which the recognition failed from among the selected content words.

TECHNICAL FIELD

The present invention relates to a sentence recognition apparatus thatuses, for example, speech recognition or text sentence recognition, asentence recognition method, a program, and a medium.

BACKGROUND ART

The prior art will be described by taking a speech recognition means asan example.

In a speech recognition means, if an error occurs due to incompleterecognition, and the result is output without correcting the error, thatwill present a serious problem in practical implementation.

To solve this problem, the prior art proposes a method in which if therecognition score of the first candidate in the recognition result isnot greater by more than a predetermined value than the recognitionscore of the second or later candidate, it is then determined that theconfidence of the recognition result is low. The sentence produced asthe recognition result is rejected or a re-entry is requested.

This example will be described in further detail with reference to anexample that uses a one-pass, n-best search which is a typical searchmeans employed, for example, in a continuous speech recognition means.

The acoustic feature of each phoneme is extracted in advance by using atraining speech DB, and the probability of connection between words eachrepresented by a string of phonemes is also computed in advance by usinga text DB. When performing recognition, the acoustic feature of inputspeech per unit time is analyzed, and the amount of the feature, in theform of a time series, is compared with the amount of the pre-learnedacoustic feature of each phoneme, to compute an acoustic score whichrepresents the probability that the input voice at each instant in timeis a phoneme.

Acoustic scores are summed in time series in accordance with the stringof phonemes in each word carried in a word dictionary, and the sum isthe acoustic score at each instant in time. If a search space for allthe phoneme strings cannot be secured, the process proceeds whileleaving only N best results ranked in order of decreasing score.

If the input voice contains a plurality of words, the words areconnected by referring to the pre-learned word connection probabilityand, when connected, the word connection probability (called thelanguage score) is added to the acoustic score.

When the recognition scores of the N best candidates are thus computed,if the difference between the first candidate and the second candidateis not larger than a predetermined value, it is determined that theconfidence of the result of the first candidate is low, and the resultis rejected (for example, Jitsuhiro et al., “Rejection by ConfidenceMeasure Based on Likelihood Difference Between Competing Phonemes”,Technical Report of IEICE, SP 97-76, pp. 1-7 (1997)).

However, the above recognition score indicates the similarity betweenthe input voice and the pre-learned acoustic model or language model,and the reality is that the value varies greatly, depending on thespeaker or on how the voice is uttered, even if correct recognition isdone. It is therefore extremely difficult to determine the score ratiothreshold for rejection, and this has often resulted in the rejection ofa correct recognition result or the output of an incorrect recognitionresult by erroneously judging it to be a correct recognition result.

As a result, it has been difficult to perform proper sentencerecognition by using speech recognition or text sentence recognition.

DISCLOSURE OF THE INVENTION

In view of the above-described problem of the prior art, it is an objectof the present invention to provide a sentence recognition apparatus, asentence recognition method, a program, and a medium, that can performproper sentence recognition by using speech recognition or text sentencerecognition.

One aspect of the present invention is a sentence recognition apparatuscomprising:

a data base for storing a plurality of predetermined standard specificword pairs each formed from a plurality of predetermined specific words;

sentence recognition means of recognizing an input sentence made up of aplurality of words;

specific word selection means of selecting said specific words fromamong the plurality of words forming said recognized sentence;

judging means of judging whether a specific word pair arbitrarily formedfrom said selected specific words matches any one of the standardspecific word pairs stored in said data base; and

erroneously recognized specific word determining means of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.

Another aspect of the present invention is a sentence recognitionapparatus, wherein said erroneously recognized specific word determiningmeans determines a specific word as being said erroneously recognizedspecific word if said specific word is found in more than apredetermined number of arbitrarily formed specific word pairs that havebeen judged as not matching any of the standard specific word pairsstored in said data base.

Still another aspect of the present invention is a sentence recognitionapparatus, further comprising re-entry requesting means of requesting,in the event of occurrence of said erroneously recognized specific word,(1) a re-entry of the specific word corresponding to said erroneouslyrecognized specific word or (2) a re-entry of said input sentence.

Yet still another aspect of the present invention is a sentencerecognition apparatus, further comprising notifying means of notifying auser of the occurrence of said erroneously recognized specific word whensaid erroneously recognized specific word does occur.

Still yet another aspect of the present invention is a sentencerecognition apparatus comprising:

a data base for storing a plurality of predetermined standard specificword pairs each formed from a plurality of predetermined specific words;

sentence recognition means of recognizing an input sentence made up of aplurality of words;

specific word selection means of selecting said specific words fromamong the plurality of words forming said recognized sentence;

judging means of judging whether a specific word pair arbitrarily formedfrom said selected specific words matches any one of the standardspecific word pairs stored in said data base; and

sentence erroneous recognition determining means of determining, basedon the result of said judgement, whether said input sentence has beenerroneously recognized or not.

A further aspect of the present invention is a sentence recognitionapparatus, further comprising sentence re-entry requesting means ofrequesting a re-entry of said input sentence in the event of occurrenceof said erroneous recognition.

A still further aspect of the present invention is a sentencerecognition apparatus, further comprising notifying means of notifying auser of the occurrence of said erroneous recognition when said erroneousrecognition does occur.

A yet further aspect of the present invention is a sentence recognitionapparatus comprising:

a first data base for storing correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong;

a second data base for storing a plurality of predetermined standardspecific word class pairs each formed from two of said predeterminedspecific word classes;

sentence recognition means of recognizing an input sentence made up of aplurality of words;

specific word selection means of selecting said specific words fromamong the plurality of words forming said recognized sentence;

specific word class determining means of determining, by utilizing thecorrespondences stored in said first data base, the specific wordclasses to which said selected specific words respectively belong;

judging means of judging whether a specific word class pair arbitrarilyformed from said determined specific word classes matches any one of thestandard specific word class pairs stored in said second data base; and

erroneously recognized specific word determining means of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.

A still yet further aspect of the present invention is a sentencerecognition apparatus, wherein said erroneously recognized specific worddetermining means determines a specific word as being said erroneouslyrecognized specific word if the specific word class to which saidspecific word belongs is found in more than a predetermined number ofarbitrarily formed specific word class pairs that have been judged asnot matching any of the standard specific word class pairs stored insaid second data base.

An additional aspect of the present invention is a sentence recognitionapparatus, further comprising re-entry requesting means of requesting,in the event of occurrence of said erroneously recognized specific word,(1) a re-entry of the specific word corresponding to said erroneouslyrecognized specific word or (2) a re-entry of said input sentence.

A still additional aspect of the present invention is a sentencerecognition apparatus, further comprising notifying means of notifying auser of the occurrence of said erroneously recognized specific word whensaid erroneously recognized specific word does occur.

A yet additional aspect of the present invention is a sentencerecognition apparatus comprising:

a first data base for storing correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong;

a second data base for storing a plurality of predetermined standardspecific word class pairs each formed from two of said predeterminedspecific word classes;

sentence recognition means of recognizing an input sentence made up of aplurality of words;

specific word selection means of selecting said specific words fromamong the plurality of words forming said recognized sentence;

specific word class determining means of determining, by utilizing thecorrespondences stored in said first data base, the specific wordclasses to which said selected specific words respectively belong;

judging means of judging whether a specific word class pair arbitrarilyformed from said determined specific word classes matches any one of thestandard specific word class pairs stored in said second data base; and

sentence erroneous recognition determining means of determining, basedon the result of said judgement, whether said input sentence has beenerroneously recognized or not.

A still yet additional aspect of the present invention is a sentencerecognition apparatus, further comprising sentence re-entry requestingmeans of requesting a re-entry of said input sentence in the event ofoccurrence of said erroneous recognition.

A supplementary aspect of the present invention is a sentencerecognition apparatus, further comprising notifying means of notifying auser of the occurrence of said erroneous recognition when said erroneousrecognition does occur.

A still supplementary aspect of the present invention is a sentencerecognition method comprising:

a storing step of storing in a data base a plurality of predeterminedstandard specific word pairs each formed from a plurality ofpredetermined specific words;

a sentence recognition step of recognizing an input sentence made up ofa plurality of words;

a specific word selection step of selecting said specific words fromamong the plurality of words forming said recognized sentence;

a judging step of judging whether a specific word pair arbitrarilyformed from said selected specific words matches any one of the standardspecific word pairs stored in said data base; and

an erroneously recognized specific word determining step of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.

A yet supplementary aspect of the present invention is a sentencerecognition method comprising:

a storing step of storing in a data base a plurality of predeterminedstandard specific word pairs each formed from a plurality ofpredetermined specific words;

a sentence recognition step of recognizing an input sentence made up ofa plurality of words;

a specific word selection step of selecting said specific words fromamong the plurality of words forming said recognized sentence;

a judging step of judging whether a specific word pair arbitrarilyformed from said selected specific words matches any one of the standardspecific word pairs stored in said data base; and

a sentence erroneous recognition determining step of determining, basedon the result of said judgement, whether said input sentence has beenerroneously recognized or not.

A still yet supplementary aspect of the present invention is a sentencerecognition method comprising:

a first storing step of storing, in a first data base, correspondencesbetween a plurality of predetermined specific words and a plurality ofspecific word classes to which said specific words belong;

a second storing step of storing in a second data base a plurality ofpredetermined standard specific word class pairs each formed from two ofsaid predetermined specific word classes;

a sentence recognition step of recognizing an input sentence made up ofa plurality of words;

a specific word selection step of selecting said specific words fromamong the plurality of words forming said recognized sentence;

a specific word class determining step of determining, by utilizing thecorrespondences stored in said first data base, the specific wordclasses to which said selected specific words respectively belong;

a judging step of judging whether a specific word class pair arbitrarilyformed from said determined specific word classes matches any one of thestandard specific word class pairs stored in said second data base; and

an erroneously recognized specific word determining step of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.

Another aspect of the present invention is a sentence recognition methodcomprising:

a first storing step of storing, in a first data base, correspondencesbetween a plurality of predetermined specific words and a plurality ofspecific word classes to which said specific words belong;

a second storing step of storing in a second data base a plurality ofpredetermined standard specific word class pairs each formed from two ofsaid predetermined specific word classes;

a sentence recognition step of recognizing an input sentence made up ofa plurality of words;

a specific word selection step of selecting said specific words fromamong the plurality of words forming said recognized sentence;

a specific word class determining step of determining, by utilizing thecorrespondences stored in said first data base, the specific wordclasses to which said selected specific words respectively belong;

a judging step of judging whether a specific word class pair arbitrarilyformed from said determined specific word classes matches any one of thestandard specific word class pairs stored in said second data base; and

a sentence erroneous recognition determining step of determining, basedon the result of said judgement, whether said input sentence has beenerroneously recognized or not.

Still another aspect of the present invention is a program for causing acomputer to carry out all or part of the steps in the sentencerecognition method, said steps comprising: the storing step of storingin a data base a plurality of predetermined standard specific word pairseach formed from a plurality of predetermined specific words; thesentence recognition step of recognizing an input sentence made up of aplurality of words; the specific word selection step of selecting saidspecific words from among the plurality of words forming said recognizedsentence; the judging step of judging whether a specific word pairarbitrarily formed from said selected specific words matches any one ofthe standard specific word pairs stored in said data base; and theerroneously recognized specific word determining step of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.

Yet still another aspect of the present invention is a program forcausing a computer to carry out all or part of the steps in the sentencerecognition method, said steps comprising: the storing step of storingin a data base a plurality of predetermined standard specific word pairseach formed from a plurality of predetermined specific words; thesentence recognition step of recognizing an input sentence made up of aplurality of words; the specific word selection step of selecting saidspecific words from among the plurality of words forming said recognizedsentence; the judging step of judging whether a specific word pairarbitrarily formed from said selected specific words matches any one ofthe standard specific word pairs stored in said data base; and thesentence erroneous recognition determining step of determining, based onthe result of said judgement, whether said input sentence has beenerroneously recognized or not.

Still yet another aspect of the present invention is a program forcausing a computer to carry out all or part of the steps in the sentencerecognition method, said steps comprising: the first storing step ofstoring, in a first data base, correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong; the second storing step of storing ina second data base a plurality of predetermined standard specific wordclass pairs each formed from two of said predetermined specific wordclasses; the sentence recognition step of recognizing an input sentencemade up of a plurality of words; the specific word selection step ofselecting said specific words from among the plurality of words formingsaid recognized sentence; the specific word class determining step ofdetermining, by utilizing the correspondences stored in said first database, the specific word classes to which said selected specific wordsrespectively belong; the judging step of judging whether a specific wordclass pair arbitrarily formed from said determined specific word classesmatches any one of the standard specific word class pairs stored in saidsecond data base; and the erroneously recognized specific worddetermining step of determining, based on the result of said judgement,an erroneously recognized specific word for which said recognitionfailed from among said selected specific words.

A further aspect of the present invention is a program for causing acomputer to carry out all or part of the steps in the sentencerecognition method, said steps comprising: the first storing step ofstoring, in a first data base, correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong; the second storing step of storing ina second data base a plurality of predetermined standard specific wordclass pairs each formed from two of said predetermined specific wordclasses; the sentence recognition step of recognizing an input sentencemade up of a plurality of words; the specific word selection step ofselecting said specific words from among the plurality of words formingsaid recognized sentence; the specific word class determining step ofdetermining, by utilizing the correspondences stored in said first database, the specific word classes to which said selected specific wordsrespectively belong; the judging step of judging whether a specific wordclass pair arbitrarily formed from said determined specific word classesmatches any one of the standard specific word class pairs stored in saidsecond data base; and the sentence erroneous recognition determiningstep of determining, based on the result of said judgement, whether saidinput sentence has been erroneously recognized or not.

A still further aspect of the present invention is a medium holdingthereon the program, wherein said medium is computer processable.

A yet further aspect of the present invention is a medium holdingthereon the program, wherein said medium is computer processable.

A still yet further aspect of the present invention is a medium holdingthereon the program, wherein said medium is computer processable.

An additional aspect of the present invention is a medium holdingthereon the program, wherein said medium is computer processable.

It will be noted that (1) in a speech recognition means that deduces anerroneously recognized word from the relations between the specificwords contained in the recognized sentence and produces an output byreflecting the result of the deduction in the recognized sentence, aresult rejecting means or a re-entry requesting means that requests theuser for a re-entry when all or many of the words used for the deductionof erroneously recognized words are deduced as being erroneouslyrecognized words, and (2) a result rejecting means or a re-entryrequesting means that requests the user for a re-entry when none or fewof the words contained in the recognized sentence match pre-learnedspecific word or word class pairs having dependency or co-occurrencerelations between them, are also included in the present invention.

Such a rejecting means comprises, for example, a continuous speechrecognition means of recognizing speech comprising a plurality of words,an important word extracting means of extracting specific words from theresult of the recognition, a confidence computing means of assessing theconfidence of the recognition result by examining the dependency orco-occurrence relations between the extracted words, a rejectiondetermining means of rejecting the result when the result lacksconfidence, and an output sentence generating means of generating are-entry requesting sentence when the result is rejected.

In this rejecting means, specific words are extracted from therecognized sentence, the extracted words are searched through for wordpairs having dependency or co-occurrence relations between them, andwhen none or few of such word pairs are found, the recognition result isrejected, thereby enabling an erroneous result to be rejectedconsistently even if the speaker or the way the voice is utteredchanges.

The result rejecting means or re-entry requesting means that uses wordclasses determined by using relations between words contained in acommonly used thesaurus dictionary and a training sentence set is alsoincluded in the present invention.

Such a rejecting means comprises, for example, a word class determiningmeans of classifying important words, a word class relationship table inwhich the relationships between word classes are defined, a continuousspeech recognition means of recognizing speech comprising a plurality ofwords, an important word extracting means of extracting specific wordsfrom the result of the recognition, a confidence computing means ofassessing the confidence of the recognition result by examining thedependency or co-occurrence relations between the extracted words, arejection determining means of rejecting the result when the resultlacks confidence, and an output sentence generating means of generatinga re-entry requesting sentence when the result is rejected.

In this rejecting means, words are optimally classified in advance, andthe dependency or co-occurrence relations between the word classes areexamined and stored in a table. When performing recognition, specificwords are extracted from the recognized sentence, the extracted wordsare searched through for word pairs having dependency or co-occurrencerelations between them by using the relationship table in which thedependency or co-occurrence relations are defined, and when none or fewof such word pairs are found, the recognition result is rejected,thereby enabling an erroneous result to be rejected consistently even ifthe speaker or the way the voice is uttered changes. Furthermore, therejection or the re-entry requesting operation can be performed evenwhen a word not contained in a sentence set used to learn therelationships between words is entered for recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the configuration of a re-entryrequesting means according to a first embodiment of the presentinvention.

FIG. 2 is a block diagram showing the configuration of a re-entryrequesting means according to a second embodiment of the presentinvention.

FIG. 3 is a block diagram showing the configuration of a speechrecognition apparatus according to a third embodiment of the presentinvention.

FIG. 4 is a diagram for explaining the data stored in a data base 101according to the third embodiment of the present invention.

FIG. 5(a) is a diagram (part 1) for explaining the operation of thespeech recognition apparatus according to the third embodiment of thepresent invention, and FIG. 5(b) is a diagram (part 2) for explainingthe operation of the speech recognition apparatus according to the thirdembodiment of the present invention.

FIG. 6 is a block diagram showing the configuration of a speechrecognition apparatus according to a fourth embodiment of the presentinvention.

FIG. 7 is a block diagram showing the configuration of a speechrecognition apparatus according to a fifth embodiment of the presentinvention.

FIG. 8(a) is a diagram for explaining the data stored in a data base 201according to the fifth embodiment of the present invention, and FIG.8(b) is a diagram for explaining the data stored in a data base 202according to the fifth embodiment of the present invention.

FIG. 9(a) is a diagram (part 1) for explaining the operation of thespeech recognition apparatus according to the fifth embodiment of thepresent invention, and FIG. 9(b) is a diagram (part 2) for explainingthe operation of the speech recognition apparatus according to the fifthembodiment of the present invention.

FIG. 10 is a block diagram showing the configuration of a speechrecognition apparatus according to a sixth embodiment of the presentinvention.

DESCRIPTION OF THE REFERENCE NUMERALS

1. TAGGED CORPUS

2. DEPENDENCY ANALYSIS

3. IMPORTANT WORD DEPENDENCY TABLE

4. IMPORTANT WORD DICTIONARY

5. SPEECH RECOGNITION

6. IMPORTANT WORD EXTRACTION

7. ERRONEOUSLY RECOGNIZED WORD DEDUCTION

8. REJECTION DETERMINATION

9. RESULT SENTENCE GENERATION

10. WORD CLASS DETERMINATION

11. IMPORTANT WORD CLASS DEPENDENCY TABLE

12. THESAURUS DICTIONARY

BEST MODE FOR CARRYING OUT THE INVENTION

Embodiments of the present invention will be described below withreference to the accompanying drawings.

(Embodiment 1)

First, referring to FIG. 1 which is a block diagram showing theconfiguration of a re-entry requesting means according to a firstembodiment of the present invention, the configuration and operation ofthe re-entry requesting means according to this embodiment will bedescribed.

The description given hereinafter deals with the case in which arecognition result is rejected by a speech recognition means.

Prior to recognition, dependency structure analysis is performed, usinga dependency analysis means, for each phrase contained in a corpus onwhich morphological analysis is already done (hereinafter called thetagged corpus) and, with the results taken to represent dependenciesbetween the content words contained in each phrase, pairs of wordshaving dependency relations between them are defined in a table. Thedependency structure analysis means here can be accomplished byperforming syntax analysis between clauses using, for example, a casegrammar rule.

When performing recognition, first, input speech is recognized and therecognition result is output as a string of words. In a continuousspeech recognition technique using a one-pass, n-best search, forexample, the string of words as the result can be output as recognitioncandidates. Next, only specific words are extracted from the sting ofwords output as the recognition result. Here, the words necessary tounderstand the main meaning of the sentence (called the important words)are extracted as the specific words; more specifically, content wordsmay be taken as the specific words. Next, an erroneously recognized wordis deduced by examining the relations between the specific words. Thisis accomplished by examining whether the important words extracted fromthe recognition result form word pairs having dependency relationsbetween them that match the already analyzed dependency relations, andby determining that an important word that does not form a correspondingword pair with any other important word is an erroneously recognizedword. In this process, if all the words are determined as beingerroneously recognized words, then it is determined that the confidenceof the recognition result is extremely low and the result shouldtherefore be rejected, and a message prompting the speaker for are-entry is generated and displayed on the screen. The message generatedin this case goes like this, “Your voice wasn't clear enough. Pleasespeak once again.” If some of the words were not deduced as beingerroneously recognized words, then a sentence consisting only of phrasescontaining the important words determined as being not erroneouslyrecognized is generated and output on the screen. This rejectiondetermining means determines that the result should be rejected when allthe words are determined as being erroneously recognized; alternatively,it may be determined that the result should be rejected when thepercentage of the erroneously recognized words in the set of importantwords extracted from the recognition result is higher than apredetermined value, otherwise the recognition result is output.

In this embodiment, the dependency relations between words are extractedin advance and, by comparing the important words contained in therecognition result against the extracted dependency relations, anerroneously recognized word that may be contained in the recognitionresult is deduced; when all or more than a predetermined percentage ofthe important words contained in the recognition result are determinedas being erroneously recognized words, then the recognition result isrejected and the speaker is prompted for a re-entry, thus enabling anerroneous result to be rejected consistently and a re-entry request tobe made in an effective manner even if the speaker or the way the voiceis uttered changes.

Accordingly, by deducing erroneously recognized words from thedependency or co-occurrence relations between the important wordscontained in the recognition result sentence, and by making a re-entryrequest to the user when many of the words are deduced as beingerroneously recognized words, it becomes possible to reject an erroneousresult consistently and issue a re-entry request in an effective mannereven if the speaker or the way the voice is uttered changes.

(Embodiment 2)

First, referring to FIG. 2 which is a block diagram showing theconfiguration of a re-entry requesting means according to a secondembodiment of the present invention, the configuration and operation ofthe re-entry requesting means according to this embodiment will bedescribed.

The description given hereinafter also deals with the case in which arecognition result is rejected by a speech recognition means.

Prior to recognition, dependency structure analysis is performed, usinga dependency analysis means, for each phrase contained in a corpus onwhich morphological analysis is already done (hereinafter called thetagged corpus) and, with the results taken to represent dependenciesbetween the content words contained in each phrase, pairs of wordshaving dependency relations between them are defined in a table. Themethod is the same as that used in the foregoing embodiment. Next, tocope with a situation in which a word not contained in the corpus isinput, the important words are classified. The classification may bedone in accordance with a commonly used thesaurus dictionary in such amanner that important words belonging to the same category in thethesaurus are grouped into the same class, but if the input domain canbe limited, further optimum classification may be achieved using thefollowing means. First, every important word is classified according tothe meaning code in the thesaurus, and this class is taken as theinitial class. In the word pairs having the already analyzed dependencyrelations, any word that is not dependent on the same word as any of thewords belonging to the same class is removed from the word class, andsuch words by themselves are made to form one class. The results of thesearch conducted on all the word pairs to locate the words to be removedfrom the class in accordance with the above condition are determined asword classes. When the word classes are thus determined, the word pairshaving the previously extracted dependency relations between them areexpressed in the form of word class pairs and stored in a table.

When performing recognition, continuous speech recognition and importantword extraction are performed in the same manner as in the foregoingembodiment, and by comparing the results against the word class pairshaving the already analyzed dependency relations between them, anerroneously recognized word is deduced, and the rejection and the outputof a re-entry request are determined.

In this embodiment, the dependency relations between the words areextracted in advance, the words are classified using the thesaurusdictionary and the dependency relations, and the above dependencyrelations are stored in the table as the dependency relations betweenthe word classes. By comparing the important words contained in therecognition result against the dependency relations between the wordclasses, erroneously recognized words contained in the recognitionresult are deduced and, when all or more than a predetermined percentageof the important words in the recognition result are determined as beingerroneously recognized words, the recognition result is rejected and thespeaker is prompted for a re-entry. Accordingly, in addition to theeffect of the foregoing embodiment that enables an erroneous result tobe rejected consistently and a re-entry request to be made in aneffective manner even if the speaker or the way the voice is utteredchanges, the present embodiment offers the effect of being able toperform the above-described processing such as the deduction oferroneously recognized words and the determination of rejection evenwhen an important word not contained in the corpus is input.

In this way, the important words are classified in advance by using thethesaurus dictionary and the corpus, and any erroneously recognized wordcontained in the recognition result is deduced by using the dependencyrelations between the important word classes and, when many of the wordsare deduced as being erroneously recognized words, a re-entry request ismade to the user; as a result, even if the speaker or the way the voiceis uttered changes, an erroneous result can be rejected consistently anda re-entry request can be made in an effective manner, and furthermore,even if an important word or a sentence expression not contained in thecorpus is input, the above described processing such as the deduction oferroneously recognized words and the determination of rejection can beperformed.

In the first and second embodiments described above, the threshold forrejection is determined, not based on the recognition score, but basedon the naturalness as a sentence of the sentence produced as therecognition result, and the dependency or co-occurrence relationsbetween the specific words contained in the recognition result areexamined and, when none of the words have much relevance to each other,the recognition result is rejected; in this way, an erroneous result canbe rejected consistently and a re-entry request made in an effectivemanner even if the speaker or the way the voice is uttered changes.

(Embodiment 3)

First, referring to FIG. 3 which is a block diagram showing theconfiguration of a speech recognition apparatus according to a thirdembodiment of the present invention, the configuration of the speechrecognition apparatus according to this embodiment will be described.

The speech recognition apparatus of this embodiment comprises a database 101, a speech recognition means 102, a content word selection means103, a judging means 104, an erroneously recognized content worddetermining means 105, and a content word re-entry requesting means 106.

Here, the data base 101 corresponds to the data base of the presentinvention, the speech recognition means 102 corresponds to the sentencerecognition means of the present invention, the content word selectionmeans 103 corresponds to the specific word selection means of thepresent invention, the judging means 104 corresponds to the judgingmeans of the present invention, the erroneously recognized content worddetermining means 105 corresponds to the erroneously recognized specificword determining means of the present invention, the content wordre-entry requesting means 106 corresponds to the re-entry requestingmeans of the present invention, and the speech recognition apparatus ofthis embodiment corresponds to the sentence recognition apparatus of thepresent invention. Further, the content word in this embodimentcorresponds to the specific word in the present invention.

Next, the configuration of the speech recognition apparatus of the thirdembodiment will be described in further detail by referring to FIG. 4which is a diagram for explaining the data stored in the database 101according to the third embodiment of the present invention.

The data base 101 is a means of storing standard content word pairs,such as , “atsui, koohii”┘, ┌, “koohii, nomu)”┘, etc., each consistingof pre-learned content words and having a predetermined keyworddependency (see FIG. 4).

The speech recognition means 102 is a means of performing speechrecognition on a voice input sentence consisting of words.

The content word selection means 103 is a means of selecting contentwords carried in a content word dictionary (not shown) from among thewords forming the sentence produced by speech recognition, by referringto the content word dictionary in which the pre-learned content wordsare defined.

The judging means 104 is a means of judging whether a content word pairarbitrarily formed by selected content words matches any one of thestandard content word pairs stored in the data base 101.

The erroneously recognized content word determining means 105 is a meansof determining that a content word is an erroneously recognized contentword if the content word is found in two or more content word pairs thathave been judged as not matching any of the standard content word pairsstored in the data base 101.

The content word re-entry requesting means 106 is a means of requesting,in the event of occurrence of an erroneously recognized content word, are-entry of the content word corresponding to the erroneously recognizedcontent word.

Next, the operation of the speech recognition apparatus according to thethird embodiment of the present will be described with reference to FIG.5(a), which is a diagram (part 1) for explaining the operation of thespeech recognition apparatus of the third embodiment, and FIG. 5(b),which is a diagram (part 2) for explaining the operation of the speechrecognition apparatus of the third embodiment. While explaining theoperation of the speech recognition apparatus of this embodiment, oneembodiment of a sentence recognition method according to the presentinvention will also be explained.

The speech recognition means 102 produces a recognized sentence ┌ “Aoikoohii wo nomi masu ka?”┘ by (erroneously) recognizing the voice inputsentence ┌ “Atsui koohii wo nomimasu ka?”┘.

The content word selection means 103 selects the content words ┌ “aoi”┘,┌ “koohii”┘, and ┌ “nomu”┘ from the words ┌ “aoi”┘, ┌ “koohii”┘ “wo”┘,“nomi”┘, ┌ “masu”┘, and ┌ “ka”┘ forming the recognized sentence ┌ “Aoikoohii wo nomi masu ka?”┘ (see FIG. 5(a)) Here, since any conjugatedcontent word is selected by taking its root form (the form appearing asan entry in the dictionary), ┌ “nomi (conjugated form) ”┘ has beenselected in the form of ┌ “nomu (root form)”┘.

The judging means 104 judges that, of a totalof three content word pairsarbitrarily formed from the selected content words, i.e., ┌, “(aoi,kooii)”┘, ┌, “(aoi, nomu)”┘, and ┌, “(kooii, nomu)”┘, the content wordpair ┌, “(kooii, nomu)”┘ is a standard content word pair stored in thedata base 101, and that the content word pairs ┌, “(aoi, kooii)”┘ and ┌,“(aoi, nomu)”┘ are not standard content word pairs stored in the database 101 (see FIG. 5(b)).

The erroneously recognized content word determining means 105 determinesthat the content word ┌ “aoi”┘ is an erroneously recognized contentword, because this content word is found in two or more arbitrarilyformed content word pairs, i.e., the arbitrarily formed content wordpairs ┌, “(aoi, koohii)”┘ and ┌, “(aoi, nomu)”┘, that have been judgedas not matching any of the standard content word pairs stored in thedata base 101.

The content word re-entry requesting means 106 requests a re-entry ofthe content word corresponding to the erroneously recognized contentword ┌ “aoi”┘.

(Embodiment 4)

First, referring to FIG. 6 which is a block diagram showing theconfiguration of a speech recognition apparatus according to a fourthembodiment of the present invention, the configuration of the speechrecognition apparatus according to this embodiment will be described.

The speech recognition apparatus of this embodiment comprises a database 101, a speech recognition means 102, a content word selection means103, a judging means 104, a sentence erroneous recognition determiningmeans 105′, and a sentence re-entry requesting means 106′.

Here, the data base 101 corresponds to the data base of the presentinvention, the speech recognition means 102 corresponds to the sentencerecognition means of the present invention, the content word selectionmeans 103 corresponds to the specific word selection means of thepresent invention, the judging means 104 corresponds to the judgingmeans of the present invention, the sentence erroneous recognitiondetermining means 105′ corresponds to the sentence erroneous recognitiondetermining means of the present invention, the sentence re-entryrequesting means 106′ corresponds to the sentence re-entry requestingmeans of the present invention, and the speech recognition apparatus ofthis embodiment corresponds to the sentence recognition apparatus of thepresent invention. Further, the content word in this embodimentcorresponds to the specific word in the present invention.

The speech recognition apparatus of the present embodiment is similar inconfiguration to the speech recognition apparatus of the foregoing thirdembodiment, but is characterized by the provision of the sentenceerroneous recognition determining means 105′ and the sentence re-entryrequesting means 106′.

Therefore, the sentence erroneous recognition determining means 105′ andthe sentence re-entry requesting means 106′ will be described in furtherdetail below.

The sentence erroneous recognition determining means 105′ is a means ofdetermining that any content word not contained in any one of thearbitrarily formed content word pairs judged to match the standardcontent word pairs stored in the data base 101 is an erroneouslyrecognized content word, and of determining that the input sentence hasbeen erroneously recognized if the relation

Y>0.4×X  (Mathematical 1)

holds between the number X of selected content words and the number Y oferroneously recognized content words.

The sentence re-entry requesting means 106′ is a means of requesting are-entry of the input sentence when erroneous recognition has occurred.

Next, the operation of the speech recognition apparatus according tothis embodiment will be described. While explaining the operation of thespeech recognition apparatus of this embodiment, one embodiment of asentence recognition method according to the present invention will alsobe explained.

The speech recognition means 102 produces a recognized sentence ┌“Jimoto no biiru wo yon de ike sen ka?”┘ by (erroneously) recognizingthe voice input sentence ┌ “Jimoto no biiru wo non de wa ike mase nka?”┘.

The content word selection means 103 selects the content words ┌“jimoto”┘, ┌ “biiru”┘, ┌ “yobu”┘, ┌ “ike”┘, and ┌ “sen”┘ from the words┌ “jimoto”┘, ┌ “no”┘, ┌ “biiru”┘, ┌ “wo”┘, ┌ “yon”┘, ┌ “de”┘, ┌ “ike”┘,┌ “sen”┘, and ┌ “ka”┘ forming the recognized sentence ┌ “Jimoto no biiruwo yon de ike sen ka?”┘. Here, since any conjugated content word isselected by taking its root form (the form appearing as an entry in thedictionary) as in the foregoing third embodiment, ┌ “yon (conjugatedform)”┘ has been selected in the form of ┌ “yobu (root form)”┘.

The judging means 104 judges that, of a total of 60 content word pairsarbitrarily formed from the selected content words, i.e., ┌, “(jimoto,biiru)”┘, ┌, “(jimoto, yobu)”┘, ┌, “jimoto, ike)”┘, ┌, “(jimoto, sen)”┘,etc., the content word pair ┌, “(jimoto, biiru)”┘ is a standard contentword pair stored in the data base 101, and that the other content wordpairs ┌, “(jimoto, yobu)”┘, ┌, “(jimoto, ike)”┘, ┌, “(jimoto, sen)”┘,etc. are not standard content word pairs stored in the data base 101.

The sentence erroneous recognition determining means 105′ determinesthat the content words ┌ “yobu”┘, ┌ “ike”┘, and ┌ “sen”┘ none of whichare contained in the arbitrarily formed content word pair ┌, “jimoto,biiru”┘ judged to be a standard content word pair stored in the database 101 are erroneously recognized content words. The sentenceerroneous recognition determining means 105′ also determines that theinput sentence ┌ “Jimoto no biiru wo non de wa ike mase n ka?”┘ has beenerroneously recognized because the relation (Mathematic 1) holds betweenthe number X of selected content words ┌ “jimoto”┘, ┌ “biiru”┘, ┌“yobu”┘, ┌ “ike”┘, and ┌ “sen”┘, which is 5, and the number Y oferroneously recognized content words ┌ “yobu”┘, ┌ “ike”┘, and ┌ “sen”┘,which is 3.

Then, the sentence re-entry requesting means 106′ requests a re-entry ofthe input sentence ┌ “Jimoto no biiru wo non de wa ike mase n ka?”┘.

(Embodiment 5)

First, referring to FIG. 7 which is a block diagram showing theconfiguration of a character recognition apparatus according to a fifthembodiment of the present invention, the configuration of the characterrecognition apparatus according to this embodiment will be described.

The character recognition apparatus of this embodiment comprises databases 201 and 202, a character recognition means 203, a content wordselection means 204, a content word class determining means 205, ajudging means 206, an erroneously recognized content word determiningmeans 207, and a content word re-entry requesting means 208.

Here, the data base 201 corresponds to the first data base of thepresent invention, the database 202 corresponds to the second data baseof the present invention, the character recognition means 203corresponds to the sentence recognition means of the present invention,the content word selection means 204 corresponds to the specific wordselection means of the present invention, the content word classdetermining means 205 corresponds to the specific word class determiningmeans of the present invention, the judging means 206 corresponds to thejudging means of the present invention, the erroneously recognizedcontent word determining means 207 corresponds to the erroneouslyrecognized specific word determining means of the present invention, thecontent word re-entry requesting means 208 corresponds to the re-entryrequesting means of the present invention, and the character recognitionapparatus of this embodiment corresponds to the sentence recognitionapparatus of the present invention. Further, the content word in thisembodiment corresponds to the specific word in the present invention,and the content word class in this embodiment corresponds to thespecific word class in the present invention.

Next, the configuration of the character recognition apparatus of thefifth embodiment will be described in further detail by referring toFIG. 8(a), which is a diagram for explaining the data stored in the database 201 according to the fifth embodiment of the present invention, andFIG. 8(b),which is a diagram for explaining the data stored in the database 202 according to the fifth embodiment of the present invention.

The data base 201 is a means of storing correspondences betweenpre-learned content words and predetermined content word classes towhich the content words belong, such as ┌ “biiru 100”┘, ┌ “koohii-100”┘,┌ “jimoto-200”┘, ┌ “atsui-200”┘, ┌ “nomu-300”┘, ┌ “yobu-400”┘, etc.Here, “90” is the meaning code assigned to the content word classcorresponding to the category “building”, “100” is the meaning codeassigned to the content word class corresponding to the category“drink”, “200” is the meaning code assigned to the content word classcorresponding to the category “property of drink”, “300” is the meaningcode assigned to the content word class corresponding to the category“action associated with drink”, and “400” is the meaning code assignedto the content word class corresponding to the category “actionassociated with voice”.

The data base 202 is a means of storing “(100, 200)”, “(100, 300)”, etc.as standard content word class pairs consisting of content word classesand having predetermined co-occurrence relations.

The character recognition means 203 is a means of recognizing charactersin a sentence consisting of words and input by means of OCR (opticalcharacter reader).

The content word selection means 204 is a means of selecting contentwords carried in a content word dictionary (not shown) from among thewords forming the character-recognized sentence, by referring to thecontent word dictionary in which the pre-learned content words aredefined.

The content word class determining means 205 is a means of determiningthe content word classes to which the selected content wordsrespectively belong, by referring to the correspondences between thecontent words and content word classes stored in the data base 201.

The judging means 206 is a means of judging whether a content word classpair arbitrarily formed by such determined content word classes matchesany one of the standard content word class pairs stored in the data base202.

The erroneously recognized content word determining means 207 is a meansof determining that a content word is an erroneously recognized contentword if the content word class to which the content word belongs isfound in two or more arbitrarily formed content word class pairs thathave been judged as not matching any of the standard content word classpairs stored in the data base 202.

The content word re-entry requesting means 208 is a means of requesting,in the event of occurrence of an erroneously recognized content word, are-entry of the content word corresponding to the erroneously recognizedcontent word.

Next, the operation of the character recognition apparatus according tothe fifth embodiment of the present will be described with reference toFIG. 9(a), which is a diagram (part 1) for explaining the operation ofthe character recognition apparatus of the fifth embodiment, and FIG.9(b), which is a diagram (part 2) for explaining the operation of thecharacter recognition apparatusof the fifth embodiment. While explainingthe operation of the character recognition apparatus of this embodiment,one embodiment of a sentence recognition method according to the presentinvention will also be explained.

The character recognition means 203 produces a recognized sentence“Jimoto no biiru wo yobi masu ka?”┘ by (erroneously) recognizing the OCRinput sentence ┌ “Jimoto no biiru wo nomi masu ka?”┘.

The content word selection means 204 selects the content words ┌“jimoto”┘, ┌ “biiru”┘, and ┌ “yobu”┘ from the words ┌ “jimoto”┘, ┌“no”┘, ┌ “biiru”┘, ┌ “wo”┘, ┌ “yobi”┘, ┌ “masu”┘, and ┌ “ka”┘ formingthe recognized sentence ┌ “Jimoto no biiru wo yobi masu ka?”┘. Here,since any conjugated content word is selected by taking its root form(the form appearing as an entry in the dictionary) as in the previouslydescribed third embodiment, ┌ “yobi (conjugated form)”┘ has beenselected in the form of ┌ “yobu (root form)”┘.

The content word class determining means 205 determines that the contentword classes to which the selected content words ┌ “jimoto”┘, ┌“biiru”┘,and ┌ “yobu”┘ are “200”, “100”, and “400”, respectively, byreferring to the correspondences between the content words and contentword classes stored in the data base 201.

The judging means 206 judges that, of a total of three content wordclass pairs arbitrarily formed from the thus determined content wordclasses, i.e., “(100, 200)”, “(100, 400)”, and “(200, 400)”, the contentword class pair “(100, 200)” is a standard content word class pairstored in the data base 202, and that the content word class pairs“(100, 400)” and “(200, 400)” are not standard content word class pairsstored in the data base 202.

The erroneously recognized content word determining means 207 determinesthat the content word ┌ “yobu”┘ is an erroneously recognized contentword, because the content word class “400” to which this content wordbelongs is found in two or more arbitrarily formed content word classpairs, i.e., the arbitrarily formed content word class pairs “(100,400)” and “(200, 400)”, that have been judged as not matching any of thestandard content word pairs stored in the data base 202.

The content word re-entry requesting means 208 requests a re-entry ofthe content word corresponding to the erroneously recognized contentword ┌ “yobu”┘.

(Embodiment 6)

First, referring to FIG. 10 which is a block diagram showing theconfiguration of a character recognition apparatus according to a sixthembodiment of the present invention, the configuration of the characterrecognition apparatus according to this embodiment will be described.

The character recognition apparatus of this embodiment comprises databases 201 and 202, a character recognition means 203, a content wordselection means 204, a content word class determining means 205, ajudging means 206, a sentence erroneous recognition determining means207′ and a sentence re-entry requesting means 208′.

Here, the data base 201 corresponds to the first data base of thepresent invention, the database 202 corresponds to the second data baseof the present invention, the character recognition means 203corresponds to the sentence recognition means of the present invention,the content word selection means 204 corresponds to the specific wordselection means of the present invention, the content word classdetermining means 205 corresponds to the specific word class determiningmeans of the present invention, the judging means 206 corresponds to thejudging means of the present invention, the sentence erroneousrecognition determining means 207′ corresponds to the sentence erroneousrecognition determining means of the present invention, the sentencere-entry requesting means 208′ corresponds to the sentence re-entryrequesting means of the present invention, and the character recognitionapparatus of this embodiment corresponds to the sentence recognitionapparatus of the present invention. Further, the content word in thisembodiment corresponds to the specific word in the present invention,and the content word class in this embodiment corresponds to thespecific word class in the present invention.

The character recognition apparatus of this embodiment is similar inconfiguration to the character recognition apparatus of the foregoingfifth embodiment, but is characterized by the provision of the sentenceerroneous recognition determining means 207′ and the sentence re-entryrequesting means 208′.

Therefore, the sentence erroneous recognition determining means 207′ andthe sentence re-entry requesting means 208′ will be described in furtherdetail below.

The sentence erroneous recognition determining means 207′ is a means ofdetermining that any content word not contained in any one of thearbitrarily formed content word class pairs judged to match the standardcontent word pairs stored in the data base 202 is an erroneouslyrecognized content word, and of determining that the input sentence hasbeen erroneously recognized if the relation

Y>0.5×X  (Mathematical 2)

holds between the number X of selected content words and the number Y oferroneously recognized content words.

The sentence re-entry requesting means 208′ is a means of requestingare-entry of the input sentence when erroneous recognition has occurred.

Next, the operation of the character recognition apparatus according tothe sixth embodiment will be described with reference to the blockdiagram of FIG. 10 showing the configuration of the characterrecognition apparatus of the sixth embodiment of the present invention.While explaining the operation of the character recognition apparatus ofthis embodiment, one embodiment of a sentence recognition methodaccording to the present invention will also be explained.

The character recognition means 203 produces a recognized sentence ┌“Jimoto no biru wo yobi mase n ka?”┘ by (erroneously) recognizing theOCR input sentence ┌ “Jimoto no biiru wo nomi mase n ka?”┘.

The content word selection means 204 selects the content words ┌“jimoto”┘, ┌ “biru”┘, and ┌ “yobu”┘ from the words ┌ “jimoto”┘, ┌ “no”┘,┌ “biru”┘, ┌ “wo”┘, ┌ “yobi”┘, ┌ “mase”┘, ┌ “n”┘, and ┌ “ka”┘ formingthe recognized sentence ┌ “Jimoto no biru wo yobi mase n ka?”┘. Here,since any conjugated content word is selected by taking its root form(the form appearing as an entry in the dictionary) as in the previouslydescribed third embodiment, ┌ “yobi (conjugated form)”┘ has beenselected in the form of ┌ “yobu (root form)”┘.

The content word class determining means 205 determines that the contentword classes to which the selected content words ┌ “jimoto”┘, ┌ “biru”┘,and ┌ “yobu”┘ are “200”, “90”, and “400”, respectively, by referring tothe correspondences between the content words and content word classesstored in the data base 201.

The judging means 206 judges that, of a total of three content wordclass pairs arbitrarily formed from the thus determined content wordclasses, i.e., “(90, 200)”, “(90, 400)”, and “(200, 400)”, there arenone that match the standard content word class pairs stored in the database 202.

The sentence erroneous recognition determining means 207′ determinesthat all the selected content words ┌ “jimoto”┘, ┌ “biru”┘, and ┌“yobu”┘ are erroneously recognized content words. The sentence erroneousrecognition determining means 207′ also determines that the inputsentence ┌ “Jimoto no biiru wo non de wa ike mase n ka?”┘ has beenerroneously recognized, because the relation (Mathematic 2) holdsbetween the number X of selected content words ┌ “jimoto”┘, ┌ “biru”┘,and ┌ “yobu”┘, which is 3, and the number Y of erroneously recognizedcontent words ┌ “jimoto”┘, ┌ “biru”┘, and ┌ “yobu”┘, which is 3.

Then, the sentence re-entry requesting means 208′ requests a re-entry ofthe input sentence ┌ “Jimoto no biiru wo nomi mase n ka?”┘.

The first to sixth embodiments have been described in detail above.

In the third and fifth embodiment described above, when there was anerroneously recognized specific word, the re-entry requesting means ofthe present invention requested a re-entry of the specific wordcorresponding to the erroneously recognized specific word, butalternatively, in the event of occurrence of an erroneously recognizedspecific word, a re-entry of the input sentence, for example, may berequested.

Of course, a notifying means may be provided that notifies the user ofthe occurrence of an erroneously recognized specific word (erroneousrecognition). For example, a message such as “The name (or a portion ofthe name) could not be heard” may be present to the user by a voice ortext; in that case also, an effect similar to the re-entry request canbe achieved.

The invention includes a program for causing a computer to carry out thefunctions of all or part of the means (or devices, elements, circuits,blocks, etc.) of the sentence recognition apparatus of the inventiondescribed above, wherein the program operates in collaboration with thecomputer. Of course, the computer here is not limited to pure hardwaresuch as a CPU, but may further include firmware, an OS, or even aperipheral device.

The invention also includes a program for causing a computer to carryout the operations in all or part of the steps (or processes,operations, effects, etc.) of the sentence recognition method of theinvention described above, wherein the program operates in collaborationwith the computer.

Here, part of the means (or devices, elements, circuits, blocks, etc.)of the invention and part of the steps (or processes, operations,effects, etc.) of the invention refer to some of the plurality of meansor steps, or some of the functions or operations in one of the means orsteps.

Further, some of the devices (or elements, circuits, blocks, etc.) ofthe invention refer to some of the plurality of devices, or some of themeans (or elements, circuits, blocks, etc.) in one of the devices, orsome of the functions in one of the means.

A computer readable recording medium with the program of the inventionrecorded thereon is also included in the present invention. In oneutilization mode of the program of the invention, the program isrecorded on a recording medium readable by a computer, and is operatedin collaboration with the computer. In another utilization mode of theprogram of the invention, the program is transmitted through atransmission medium, is read by a computer, and is operated incollaboration with the computer. The recording medium includes a ROM orthe like, and the transmission medium includes a transmission mediumsuch as the Internet, light waves, radio waves, or sound waves.

The configuration of the invention may be implemented in software or inhardware.

The invention also includes a medium having a program recorded thereonfor causing a computer to carry out all or some of the functions of allor some of the means of the sentence recognition apparatus of theinvention described above, wherein the program readable by the computeris read by the computer and carries out the functions in collaborationwith the computer.

The invention further includes a medium having a program recordedthereon for causing a computer to carry out all or some of theoperations in all or some of the steps of the sentence recognitionmethod of the invention described above, wherein the program readable bythe computer is read by the computer and carries out the operations incollaboration with the computer.

The entire disclosure of the above literature is incorporated herein byreference in its entirety.

Potential for Exploitation in Industry

As is apparent from the above description, the present invention has theadvantage of being able to perform proper sentence recognition by usingspeech recognition or text sentence recognition.

What is claimed is:
 1. A sentence recognition apparatus comprising: adata base for storing a plurality of predetermined standard specificword pairs each formed from a plurality of predetermined specific words;sentence recognition means of recognizing an input sentence made up of aplurality of words; specific word selection means of selecting saidspecific words from among the plurality of words forming said recognizedsentence; judging means of judging whether a specific word pairarbitrarily formed from said selected specific words matches any one ofthe standard specific word pairs stored in said data base; anderroneously recognized specific word determining means of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.
 2. A sentence recognition apparatus as set forth inclaim 1, wherein said erroneously recognized specific word determiningmeans determines a specific word as being said erroneously recognizedspecific word if said specific word is found in more than apredetermined number of arbitrarily formed specific word pairs that havebeen judged as not matching any of the standard specific word pairsstored in said data base.
 3. A sentence recognition apparatus as setforth in claim 1 or 2, further comprising re-entry requesting means ofrequesting, in the event of occurrence of said erroneously recognizedspecific word, (1) a re-entry of the specific word corresponding to saiderroneously recognized specific word or (2) a re-entry of said inputsentence.
 4. A sentence recognition apparatus as set forth in claim 1 or2, further comprising notifying means of notifying a user of theoccurrence of said erroneously recognized specific word when saiderroneously recognized specific word does occur.
 5. A sentencerecognition apparatus comprising: a data base for storing a plurality ofpredetermined standard specific word pairs each formed from a pluralityof predetermined specific words; sentence recognition means ofrecognizing an input sentence made up of a plurality of words; specificword selection means of selecting said specific words from among theplurality of words forming said recognized sentence; judging means ofjudging whether a specific word pair arbitrarily formed from saidselected specific words matches any one of the standard specific wordpairs stored in said data base; and sentence erroneous recognitiondetermining means of determining, based on the result of said judgement,whether said input sentence has been erroneously recognized or not.
 6. Asentence recognition apparatus as set fourth in claim 5, furthercomprising sentence re-entry requesting means of requesting a re-entryof said input sentence in the event of occurrence of said erroneousrecognition.
 7. A sentence recognition apparatus as set fourth in claim5, further comprising notifying means of notifying a user of theoccurrence of said erroneous recognition when said erroneous recognitiondoes occur.
 8. A sentence recognition apparatus comprising: a first database for storing correspondences between a plurality of predeterminedspecific words and a plurality of specific word classes to which saidspecific words belong; a second data base for storing a plurality ofpredetermined standard specific word class pairs each formed from two ofsaid predetermined specific word classes; sentence recognition means ofrecognizing an input sentence made up of a plurality of words; specificword selection means of selecting said specific words from among theplurality of words forming said recognized sentence; specific word classdetermining means of determining, by utilizing the correspondencesstored in said first data base, the specific word classes to which saidselected specific words respectively belong; judging means of judgingwhether a specific word class pair arbitrarily formed from saiddetermined specific word classes matches any one of the standardspecific word class pairs stored in said second data base; anderroneously recognized specific word determining means of determining,based on the result of said judgement, an erroneously recognizedspecific word for which said recognition failed from among said selectedspecific words.
 9. A sentence recognition apparatus as set forth inclaim 8, wherein said erroneously recognized specific word determiningmeans determines a specific word as being said erroneously recognizedspecific word if the specific word class to which said specific wordbelongs is found in more than a predetermined number of arbitrarilyformed specific word class pairs that have been judged as not matchingany of the standard specific word class pairs stored in said second database.
 10. A sentence recognition apparatus as set forth in claim 8 or 9,further comprising re-entry requesting means of requesting, in the eventof occurrence of said erroneously recognized specific word, (1) are-entry of the specific word corresponding to said erroneouslyrecognized specific word or (2) a re-entry of said input sentence.
 11. Asentence recognition apparatus as set forth in claim 8 or 9, furthercomprising notifying means of notifying a user of the occurrence of saiderroneously recognized specific word when said erroneously recognizedspecific word does occur.
 12. A sentence recognition apparatuscomprising: a first data base for storing correspondences between aplurality of predetermined specific words and a plurality of specificword classes to which said specific words belong; a second data base forstoring a plurality of predetermined standard specific word class pairseach formed from two of said predetermined specific word classes;sentence recognition means of recognizing an input sentence made up of aplurality of words; specific word selection means of selecting saidspecific words from among the plurality of words forming said recognizedsentence; specific word class determining means of determining, byutilizing the correspondences stored in said first data base, thespecific word classes to which said selected specific words respectivelybelong; judging means of judging whether a specific word class pairarbitrarily formed from said determined specific word classes matchesany one of the standard specific word class pairs stored in said seconddata base; and sentence erroneous recognition determining means ofdetermining, based on the result of said judgement, whether said inputsentence has been erroneously recognized or not.
 13. A sentencerecognition apparatus as set fourth in claim 12, further comprisingsentence re-entry requesting means of requesting a re-entry of saidinput sentence in the event of occurrence of said erroneous recognition.14. A sentence recognition apparatus as set fourth in claim 12, furthercomprising notifying means of notifying a user of the occurrence of saiderroneous recognition when said erroneous recognition does occur.
 15. Asentence recognition method comprising the steps of: storing in a database a plurality of predetermined standard specific word pairs eachformed from a plurality of predetermined specific words; recognizing aninput sentence made up of a plurality of words; selecting said specificwords from among the plurality of words forming said recognizedsentence; judging whether a specific word pair arbitrarily formed fromsaid selected specific words matches any one of the standard specificword pairs stored in said data base; and determining, based on theresult of said judgement, an erroneously recognized specific word forwhich said recognition failed from among said selected specific words.16. A program for causing a computer to carry out all or part of thesteps in the sentence recognition method of claim 15, said stepscomprising: storing in a data base a plurality of predetermined standardspecific word pairs each formed from a plurality of predeterminedspecific words; recognizing an input sentence made up of a plurality ofwords; selecting said specific words from among the plurality of wordsforming said recognized sentence; judging whether a specific word pairarbitrarily formed from said selected specific words matches any one ofthe standard specific word pairs stored in said data base; anddetermining, based on the result of said judgement, an erroneouslyrecognized specific word for which said recognition failed from amongsaid selected specific words.
 17. A medium holding thereon the programof claim 16, wherein said medium is computer processable.
 18. A sentencerecognition method comprising the steps of: storing in a data base aplurality of predetermined standard specific word pairs each formed froma plurality of predetermined specific words; recognizing an inputsentence made up of a plurality of words; selecting said specific wordsfrom among the plurality of words forming said recognized sentence;judging whether a specific word pair arbitrarily formed from saidselected specific words matches any one of the standard specific wordpairs stored in said data base; and determining, based on the result ofsaid judgement, whether said input sentence has been erroneouslyrecognized or not.
 19. A program for causing a computer to carry out allor part of the steps in the sentence recognition method of claim 18,said steps comprising: storing in a data base a plurality ofpredetermined standard specific word pairs each formed from a pluralityof predetermined specific words; recognizing an input sentence made upof a plurality of words; selecting said specific words from among theplurality of words forming said recognized sentence; judging whether aspecific word pair arbitrarily formed from said selected specific wordsmatches any one of the standard specific word pairs stored in said database; and determining, based on the result of said judgement, whethersaid input sentence has been erroneously recognized or not.
 20. A mediumholding thereon the program of claim 19, wherein said medium is computerprocessable.
 21. A sentence recognition method comprising the steps of:storing, in a first data base, correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong; storing in a second data base aplurality of predetermined standard specific word class pairs eachformed from two of said predetermined specific word classes; recognizingan input sentence made up of a plurality of words; selecting saidspecific words from among the plurality of words forming said recognizedsentence; determining, by utilizing the correspondences stored in saidfirst data base, the specific word classes to which said selectedspecific words respectively belong; judging whether a specific wordclass pair arbitrarily formed from said determined specific word classesmatches any one of the standard specific word class pairs stored in saidsecond data base; and determining, based on the result of saidjudgement, an erroneously recognized specific word for which saidrecognition failed from among said selected specific words.
 22. Aprogram for causing a computer to carry out all or part of the steps inthe sentence recognition method of claim 21, said steps comprising:storing, in a first data base, correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong; storing in a second data base aplurality of predetermined standard specific word class pairs eachformed from two of said predetermined specific word classes; recognizingan input sentence made up of a plurality of words; selecting saidspecific words from among the plurality of words forming said recognizedsentence; determining, by utilizing the correspondences stored in saidfirst data base, the specific word classes to which said selectedspecific words respectively belong; judging whether a specific wordclass pair arbitrarily formed from said determined specific word classesmatches any one of the standard specific word class pairs stored in saidsecond data base; and the erroneously recognized specific worddetermining step of determining, based on the result of said judgement,an erroneously recognized specific word for which said recognitionfailed from among said selected specific words.
 23. A medium holdingthereon the program of claim 22, wherein said medium is computerprocessable.
 24. A sentence recognition method comprising the steps of:storing, in a first data base, correspondences between a plurality ofpredetermined specific words and a plurality of specific word classes towhich said specific words belong; storing in a second data base aplurality of predetermined standard specific word class pairs eachformed from two of said predetermined specific word classes; recognizingan input sentence made up of a plurality of words; selecting saidspecific words from among the plurality of words forming said recognizedsentence; determining, by utilizing the correspondences stored in saidfirst data base, the specific word classes to which said selectedspecific words respectively belong; judging whether a specific wordclass pair arbitrarily formed from said determined specific word classesmatches any one of the standard specific word class pairs stored in saidsecond data base; and determining, based on the result of saidjudgement, whether said input sentence has been erroneously recognizedor not.
 25. A program for causing a computer to carry out all or part ofthe steps in the sentence recognition method of claim 24, said stepscomprising: storing, in a first data base, correspondences between aplurality of predetermined specific words and a plurality of specificword classes to which said specific words belong; storing in a seconddata base a plurality of predetermined standard specific word classpairs each formed from two of said predetermined specific word classes;recognizing an input sentence made up of a plurality of words; selectingsaid specific words from among the plurality of words forming saidrecognized sentence; determining, by utilizing the correspondencesstored in said first data base, the specific word classes to which saidselected specific words respectively belong; judging whether a specificword class pair arbitrarily formed from said determined specific wordclasses matches any one of the standard specific word class pairs storedin said second data base; and determining, based on the result of saidjudgement, whether said input sentence has been erroneously recognizedor not.
 26. A medium holding thereon the program of claim 25, whereinsaid medium is computer processable.